Pear fruit typically contains abundant highly lignified cells, known as stone cells, which have a negative impact on the fruit's edibility and processing quality. Despite extensive physiological and molecular research, there remains a limited understanding of the precise spatiotemporal aspects of lignification in flesh cells during pear development, particularly regarding the initiation of lignification and expansion of stone cell clusters. Here, an emerging bioorthogonal chemistry-based imaging technique was employed to in vivo visualize cell lignification dynamics in developing pear fruit. Specific identification of active sites undergoing lignification revealed that initial lignification of flesh cells occurred at 10 days after full bloom (DAFB), resulting in the formation of primordial stone cells (PSCs). These PSCs exhibited a random distribution and showed significantly larger diameter and area compared to normal parenchyma cells. Subsequently, PSCs developed pit canals and initiated lignification process in their neighboring cells at 15 DAFB. A cascading effect in the formation of stone cell aggregations was visualized by tracing of the lignification trajectory. This expansion process exhibited a domino effect, whereby lignification progressively spread from one cell to the next, creating a cascading pattern of stone cell formation. Finally, a cellular developmental model was proposed for stone cell formation. This study presented a procedure for applying the cutting-edge technology, click chemistry imaging, to get insights into practical scientific questions. The findings elucidated the spatiotemporal dynamics of active lignification sites in pear fruit at the cellular level, thereby enhancing our understanding of the initiation and aggregation processes in stone cell formation.

The leaf area-to-fruit ratio (LAFR) is an important factor affecting fruit quality. Previous studies on LAFR have provided some recommendations for optimal values. However, these recommendations have been quite broad and lack effectiveness during the fruit thinning period. In this study, data on the LAFR and fruit quality of pears at 5 stages were collected by continuously girdling bearing branches throughout the entire fruit development process. Five different clustering algorithms, including KMeans, Agglomerative clustering, Spectral clustering, Birch, and Spectral biclustering, were employed to classify the fruit quality data. Agglomerative clustering yielded the best results when the dataset was divided into 4 clusters. The least squares method was utilized to fit the LAFR corresponding to the best quality cluster, and the optimal LAFR values for 28, 42, 63, 91, and 112 days after flowering were 12.54, 18.95, 23.79, 27.06, and 28.76 dm² (the corresponding leaf-to-fruit ratio values were 19, 29, 36, 41, and 44, respectively). Furthermore, field verification experiments demonstrated that the optimal LAFR contributed to improving pear fruit quality, and a relatively high LAFR beyond the optimum value did not further increase quality. In summary, we optimized the LAFR of pear trees at different stages and confirmed the effectiveness of the optimal LAFR in improving fruit quality. Our research provides a theoretical basis for managing pear tree fruit load and achieving high-quality, clean fruit production.

Crop breeding programs generate large datasets. Thus, it is difficult to ensure the accuracy and integrity of all the collected data in the breeding process. To improve breeding efficiency, we established an open source and free breeding evaluation information system (BreedingEIS). The full system is composed of a web client and a mobile client. The web client is used to name the individual breeding offspring plants and analyze data. The mobile client is based on the technology of widely used smartphones and is suitable for Android and iOS systems. Its functions focus on field evaluation, including quick response code recognition, evaluation data entry, and real-time viewing. In addition, near-field communication technology and portable label machines are introduced to enable breeders to quickly locate each individual plant and accurately label any samples collected from it. Generally, BreedingEIS enables users to accurately and conveniently register phenotypic data and quickly lock target individual plants from large volumes of data. The system provides a low-cost and highly efficient solution for crop information evaluation and enables breeders to better collect, manage, and use breeding data for decision making, which is a valuable resource for crop breeding.